Describe and evaluate Kelley’s covariation-based account of causal attribution

The study of causal attributions aims to infer the processes by which people interpret why things happen in the world around them (Baron & Byrne, 2000). What may a person attribute their exam failure to? Would an observer of a car crash ascribe the causes to one particular driver, the vehicles involved, the specific circumstances on the road, or to one of numerous other sources? Kelley (1967; 1971; 1972 as cited in Kelley 1973) developed Heider’s (1958, as cited in Kelley, 1973) theory that humans act as na�ve scientists attempting to correctly determine causality in everyday life. In view of this Kelley (1973) built his covariation-based theory of causal attribution around the statistical technique of analysis of variance (ANOVA) suggesting that when a perceiver has the opportunity to make multiple observations they determine their attributions within this scientifically based framework. A critical evaluation of Kelley’s (1973) covariation-based account of attribution, and McArthur’s (1972) subsequent experimental verification of this theory will be given. The covariation-based model will be interpreted with respect to the ANOVA framework employed, with the view that an integrative model of attribution is needed to account for the biases that people bring to bear in the process of attributing causality.

Kelley’s (1967; 1971; 1972 as cited in Kelley 1973) attribution theory stems from the covariation principle that ” an effect is attributed to one of its possible causes with which, over time, it covaries” (Kelley, 1973, p. 108). Furthermore, Kelley (1973) suggested that the majority of attribution problems vary in the extent to which they are effected by three possible causes: persons, times and entities. Kelley termed information known about these 3 causes as “consensus” information, relating to the variations over different persons, “consistency” information, concerning the variations in outcome over different time or modalities, and “distinctiveness” information, regarding the extent to which effects vary over different stimuli. Consider a situation such as ‘Jack smiles at the girl’. How would we evaluate why this event occurred? We may also know that ‘everyone else smiles at the girl’, which would reflect high consensus. Furthermore, we may know that ‘in the past Jack has always smiled at the girl, reflecting high consistency. Finally, if ‘Jack does not smile at any other girl’ the information is high in distinctiveness. Therefore, when employing a layperson’s version of the ANOVA model to aid attribution, consensus, consistency and distinctiveness information becomes the independent variables and the effect (e.g. Jack smiling) constitutes the dependent variable (Kelley, 1973).

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Using the concept that consensus, consistency and distinctiveness are evaluated using the ANOVA model, Kelley (1967; 1971; 1972 as cited in Kelley 1973) predicted the patterns of information that would lead to specific attributions. He suggested that an information pattern of high consensus, high consistency and high distinctiveness (as seen in the scenario above) would prompt an attribution of causality to the particular entity (in this case the girl). Kelley (1973) also suggested that a pattern of low consensus, high consistency and low distinctiveness (e.g. Jack, alone smiles at the girl, he has always smiled at the girl yet he also smiles at other girls) suggests an attribution in terms of the person (i.e. Jack). Finally, Kelley (1973) suggested that an attribution is made in terms of the particular circumstances at that time if consensus is low, consistency is low and distinctiveness is high (e.g. Jack, alone smiles at the girl, he doesn’t usually smile at the girl, and he doesn’t smile at other girls).

Although Kelley predicted the attributions that may arise from certain combinations of consensus, consistency and distinctiveness information, he never verified them through experimentation. Consequently, the first investigation into Kelley’s predictions by McArthur (1972) is notable as it grounded much subsequent research into the covariation-based theory of attribution. McArthur’s (1972) study allocated either high or low values to each of the three types of information, thus producing eight possible patterns of attribution data. The attributions made by participants about these configurations were consistent with Kelley’s (1967; 1971; 1972 as cited in Kelley 1973) predictions.

More recent, naturalistic investigations into Kelley’s attribution model only partially support covariation-based theory. Peterson’s (1980) archival investigation into attributions for victory and defeat in a sporting newspaper found that football players’ and coaches’ ignored obvious covariation in some instances. Subsequently, although successful teams attributed a win late in the season to themselves and unsuccessful teams attributed a late-season defeat to themselves, these covariation-congruent attributions did not extend to other circumstances. Notably, successful teams attributed a late defeat to factors within the teams, and unsuccessful teams ascribed a late win to themselves although the obvious covariation throughout the season refuted these attributions. However, it is debatable whether public statements of attribution made in a newspaper accurately reflect processes in the more personal types of attribution Kelley (1973) theorized about.

Peterson’s (1980) experiment is not the only investigation that does not wholly verify a covariation-based attribution theory. Much evidence has been cited concerning the limitations of Kelley’s (1967; 1971; 1972 as cited in Kelley 1973) theory and the methodology used by McArthur in the verification of predictions made by the ANOVA model. Prototypical instances. Unnaturalistic?

McArthur’s (1972) study provides a large empirical basis for support of Kelley’s covariation model (Hewstone, 1989). However, methodological flaws and limitations in McArthur’s investigations are evident. As noted by the author herself, this investigation of Kelley’s predictions only assesses attributions made about the behavior of other people, despite the fact that Kelley intended his model to apply to ones own behavior as well as that of another person (McArthur, 1972). Equally, McArthur observed that her study would only asses judgements made by participants when they provided with prepackaged information about a certain event. However, in real life attribution processes participants do not receive such neat informational inputs (Fischhoff, 1976, as cited in Major, 1980) and may not actually search for or employ information on consensus, consistency or distinctiveness (McArthur, 1972). Cordray and Shaw (1978) suggest that this is a fundamental flaw in the covariation theory and a drawback for McArthur’s methodology. They suggest that the use of a within-subjects design may have prompted participants to believe that they should respond differently to each information configuration. Furthermore, the very structured stimuli used would encourage participants to give a response congruent with standard logic (Cordray & Shaw, 1978). It has been suggested that “evidence that subjects utilize covariation information to infer causality under conditions that encourage subjects to behave logically merely shows that they can be logical” (Cordray & Shaw, 1978, p.281). Therefore, a test of whether participants actually use covariation information, rather than whether they are capable of using, it is needed (Cordray & Shaw, 1978).

Several studies have investigated participants’ actual utilization of consensus, consistency and distinctiveness data when the information is not given in a prepackaged form. The results prove problematic for the covariation-based model of causal attribution (Hewstone, Strobe & Stephenson, 1996). Garland, Hardy and Stephenson (1975, as cited in Major, 1980) asked participants what types of information they would like if they had to make an attribution about a particular circumstance. Only 23% of the requests made by participants could be allocated to one of the informational categories described by Kelley. Furthermore, Major (1980) found that when provided with a total of 36 pieces of consensus, consistency and distinctiveness information (12 pieces from each category) to base their attributions, participants sampled, on average, only 9 pieces. In addition to this, 23% of participants failed to sample from at least one category, with the highest total of 17% of participants failing to utilize consensus information in their attributions.

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